SMOP
is Small Matlab and Octave to Python compiler.SMOP
translates matlab to python. Despite obvious similarities
between matlab and numeric python, there are enough differences to
make manual translation infeasible in real life.SMOP
generates
humanreadable python, which also appears to be faster than octave.
Just how fast? Timing results for “Moving furniture” are shown
in Table 1. It seems that for this program, translation to python
resulted in about two times speedup, and additional two times speedup
was achieved by compilingSMOP
runtime libraryruntime.py
to C, using cython. This pseudobenchmark measures scalar
performance, and my interpretation is that scalar computations are
of less interest to the octave team.
octave3.8.1  190 ms 
smop+python2.7  80 ms 
smop+python2.7+cython0.20.1  40 ms 
Table 1. SMOP performance 
News
 October 15, 2014
 Version 0.26.3 is available for beta testing.
Next version 0.27 is planned to compile octave
scripts
library, which contains over 120 KLOC in
almost 1,000 matlab files. There are 13 compilation
errors with smop 0.26.3 .
Installation

Network installation is the best method if you just want it to
run the example:$ easy_install smop user

Install from the sources if you are behind a firewall:
$ tar zxvf smop.tar.gz $ cd smop $ python setup.py install user

Fork github repository if you need the latest fixes.

Finally, it is possible to use smop without doing the installation,
but only if you already installed the dependences — numpy
and networkx:$ tar zxvf smop.tar.gz $ cd smop/smop $ python main.py solver.m $ python solver.py
Working example
We will translate solver.m
to present a sample of smop features. The
program was borrowed from the matlab programming competition in 2004 (Moving
Furniture).To the left is solver.m
. To the right is a.py
— its
translation to python. Though only 30 lines long, this
example shows many of the complexities of converting matlab code
to python.
01 function mv = solver(ai,af,w) 01 def solver_(ai,af,w,nargout=1):
02 nBlocks = max(ai(:)); 02 nBlocks=max_(ai[:])
03 [m,n] = size(ai); 03 m,n=size_(ai,nargout=2)
02  Matlab uses round brackets both for array indexing and for function calls. To figure out which is which, SMOP computes local usedef information, and then applies the following rule: undefined names are functions, while defined are arrays. 
03  Matlab function size returns variable number ofreturn values, which corresponds to returning a tuple in python. Since python functions are unaware of the expected number of return values, their number must be explicitly passed in nargout . 
04 I = [0 1 0 1]; 04 I=matlabarray([0,1,0, 1])
05 J = [1 0 1 0]; 05 J=matlabarray([1,0, 1,0])
06 a = ai; 06 a=copy_(ai)
07 mv = []; 07 mv=matlabarray([])
04  Matlab array indexing starts with one; python indexing starts with zero. New class matlabarray derives fromndarray , but exposes matlab array behaviour. Forexample, matlabarray instances always have at leasttwo dimensions — the shape of I and J is [1 4]. 
06  Matlab array assignment implies copying; python assignment implies data sharing. We use explicit copy here. 
07  Empty matlabarray object is created, and thenextended at line 28. Extending arrays by outofbounds assignment is deprecated in matlab, but is widely used never the less. Python ndarray can’t be resized except in some special cases. Instances of matlabarray can be resized exceptwhere it is too expensive. 
08 while ~isequal(af,a) 08 while not isequal_(af,a):
09 bid = ceil(rand*nBlocks); 09 bid=ceil_(rand_() * nBlocks)
10 [i,j] = find(a==bid); 10 i,j=find_(a == bid,nargout=2)
11 r = ceil(rand*4); 11 r=ceil_(rand_() * 4)
12 ni = i + I(r); 12 ni=i + I[r]
13 nj = j + J(r); 13 nj=j + J[r]
09  Matlab functions of zero arguments, such asrand , can be used without parentheses. In python,parentheses are required. To detect such cases, used but undefined variables are assumed to be functions. 
10  The expected number of return values from the matlab function find is explicitly passed in nargout . 
12  Variables I and J contain instances of the new classmatlabarray , which among other features uses onebased array indexing. 
14 if (ni<1)  (ni>m)  14 if (ni < 1) or (ni > m) or
(nj<1)  (nj>n) (nj < 1) or (nj > n):
15 continue 15 continue
16 end 16
17 if a(ni,nj)>0 17 if a[ni,nj] > 0:
18 continue 18 continue
19 end 19
20 [ti,tj] = find(af==bid); 20 ti,tj=find_(af == bid,nargout=2)
21 d = (tii)^2 + (tjj)^2; 21 d=(ti  i) ** 2 + (tj  j) ** 2
22 dn = (tini)^2 + (tjnj)^2; 22 dn=(ti  ni) ** 2 + (tj  nj) ** 2
23 if (d<dn) && (rand>0.05) 23 if (d < dn) and (rand_() > 0.05):
24 continue 24 continue
25 end 25
26 a(ni,nj) = bid; 26 a[ni,nj]=bid
27 a(i,j) = 0; 27 a[i,j]=0
28 mv(end+1,[1 2]) = [bid r]; 28 mv[mv.shape[0] + 1,[1,2]]=[bid,r]
29 end 29
30 30 return mv
Implementation status
Random remarks
 With less than five thousands lines of python code
SMOP
does not pretend to compete with such polished
products as matlab or octave. Yet, it is not a toy.
There is an attempt to follow the original matlab
semantics as close as possible. Matlab language
definition (never published afaik) is full of dark
corners, andSMOP
tries to follow matlab as
precisely as possible. There is a price, too.
 The generated sources are
matlabic, rather than pythonic, which means that
library maintainers must be fluent in both languages,
and the old development environment must be kept around.  Should the generated program be pythonic or matlabic?

For example should array indexing start with zero
(pythonic) or with one (matlabic)?I beleive now that some matlabic accent is unavoidable
in the generated python sources. Imagine matlab program
is using regular expressions, matlab style. We are not
going to translate them to python style, and that code
will remain forever as a reminder of the program’s
matlab origin.Another example. Matlab code opens a file; fopen
returns 1 on error. Pythonic code would raise
exception, but we are not going to do that. Instead,
we will live with the accent, and smop takes this to the
extreme — the matlab program remains mostly unchanged.It turns out that generating matlabic` allows for
moving much of the project complexity out of the
compiler (which is already complicated enough) and into
the runtime library, where there is almost no
interaction between the library parts.
 Which one is faster — python or octave? I don’t know.
 Doing reliable performance measurements is notoriously
hard, and is of low priority for me now. Instead, I wrote
a simple drivergo.m
andgo.py
and rewrote rand
so that python and octave versions run the same code.
Then I ran the above example on my laptop. The results
are twice as fast for the python version. What does it
mean? Probably nothing. YMMV.
ai = zeros(10,10);
af = ai;
ai(1,1)=2;
ai(2,2)=3;
ai(3,3)=4;
ai(4,4)=5;
ai(5,5)=1;
af(9,9)=1;
af(8,8)=2;
af(7,7)=3;
af(6,6)=4;
af(10,10)=5;
tic;
mv = solver(ai,af,0);
toc
Running the test suite:
$ cd smop $ make check $ make test
Commandline options
[email protected] ~/smopgithub/smop $ python main.py h
SMOP compiler version 0.25.1
Usage: smop [options] filelist
Options:
V version
X exclude=FILES Ignore files listed in commaseparated list FILES
d dot=REGEX For functions whose names match REGEX, save debugging
information in "dot" format (see www.graphviz.org).
You need an installation of graphviz to use dot
option. Use "dot" utility to create a pdf file.
For example:
$ python main.py fastsolver.m d "solvercbest"
$ dot Tpdf o resolve_solver.pdf resolve_solver.dot
h help
o output=FILENAME By default create file named a.py
o output= Use standard output
s strict Stop on the first error
v verbose